New software developed at Rensselaer predicts promising ingredients for new drugs

een large databases, accurately predicting the molecules that show potential.

"Conventional techniques are not truly predictive and don't work," Bennett said. "So we borrowed pattern recognition techniques already used in the pharmaceutical industry and added algorithms based on support vector machines. That gives us a technique to predict which molecules are promising."

Rensselaer researchers noted that predictive modeling is one of a new breed of drug discovery methods that marks a shift in industry practice--a shift away from cell-based assays performed in the lab toward math-based models calculated on the computer.

"Our program allows researchers to 'crash test' lots of molecules quickly and inexpensively," Breneman said. "That prevents a lot of false starts. The ultimate pay-off of this methodology may be that it can support the rapid invention of new drugs when diseases develop quickly and threaten society."

As drug makers increasingly target complex, chronic illness, drug development becomes far more costly and time consuming. Meanwhile, in the search for new drugs, 99.9 percent of compounds tested ultimately fail. Accordingly, drug makers want to be able to predict more accurately which compounds will produce the next blockbuster drug.

The Rensselaer research team will continue work to improve drug discovery methods, which will be carried out in the new Rensselaer Center for Biotechnology and Interdisciplinary Studies, a state-of-the-art facility scheduled to open in September 2004.

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